[1]XU Jinchao,YANG Cuili,QIAO Junfei,et al.Dissolved oxygen concentration control method based on self-organizing fuzzy neural network[J].CAAI Transactions on Intelligent Systems,2018,13(6):905-912.[doi:10.11992/tis.201801019]
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Dissolved oxygen concentration control method based on self-organizing fuzzy neural network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 6 Page number: 905-912 Column: 学术论文—机器学习 Public date: 2018-10-25
Title:
Dissolved oxygen concentration control method based on self-organizing fuzzy neural network
Author(s):
XU Jinchao1; 2;  YANG Cuili1; 2;  QIAO Junfei1; 2;  MA Shijie1; 2
1. Faculty of Information Technology, Beijng University of technology, Beijing 100124, China;
2. Beijing Key Laboratory of Computational Intelligence and Intelligence System, Beijing 100124, China
Keywords:
wastewater treatment; dissolved oxygen; process control; neural network; self-organization
CLC:
TP183
DOI:
10.11992/tis.201801019
Abstract:
It is difficult to control the dissolved oxygen (DO) concentration in wastewater treatment processes. To solve this problem, this paper proposes a dissolved oxygen control method based on a self-organizing fuzzy neural network (SOFNN). First, two judging criteria, firing strength and neuron importance, were used to determine the contribution and activity of neurons to the network. Then the inactive neurons were deleted to adjust the structure of the neural network to adaptively meet the actual control requirements and improve control accuracy. Second, a gradient descent algorithm was used to update the SOFNN parameters to ensure accuracy of the neural network. Finally, the proposed algorithm was used for the Mackey-Glass time series prediction, and the results showed that the proposed SOFNN had better prediction performance. Furthermore, the proposed SOFNN method was used on the benchmark simulation model no.1 (BSM1). The results indicate that the proposed SOFNN controller can achieve a better control effect for the DO control and has a good adaptive ability.
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